半導體製造規格不斷的縮小、以及堆疊層數增加的情況下，使得化學機械拋光製程(Chemical Mechanical Polishing, CMP)對臨界尺寸控制需更加要求，因此在製程終點控制面臨重大挑戰。本研究針對拋光終點偵測(Endpoint detection, EPD)建立卷積神經網路(Convolutional neural network, CNN)線上終點辨別以及離線拋光訊號預測。於銅膜晶圓(Copper blanket wafer)拋光，量測拋光馬達扭矩、聲音訊號、震動訊號，進行訊號分析，發現馬達扭矩訊號可辨別銅膜移除至阻障層(Barrier layer)露出時的訊號差異，再以馬達扭矩訊號進一步建立線上Labview拋光量測系統，將收集到的拋光扭矩，訓練卷積神經網路模型。實驗進行40×40 mm2銅膜晶圓拋光線上終點偵測以及離線拋光訊號預測，於40×40 mm2銅膜晶圓拋光結果，卷積神經網路的終點辨識，較材料移除率(MRR)的終點準確，拋光頭及拋光盤離線訊號預測的均方誤差平均可達4.97×10^-7、9.61×10^-8。最後進行8吋銅膜晶圓拋光，結果在拋光頭及拋光盤離線訊號預測的均方誤差平均分別可達8.97×10^-6、2.24×10^-7。本研究之CNN方法可有效預測CMP成訊號。

The shrinkage of IC chips and increasing of stacked layers in semiconductor manufacturing make more demanding for the precision of the critical dimension. The chemical mechanical polishing (CMP) process faces a challenge in precise endpoint detection (EPD). This research focuses on convolutional neural network (CNN) of CMP EPD system. The offline signal analysis is performed by motor torque, acoustic emission (AE), and vibration signal during CMP process. Result shows that the motor torque signal of CMP can identify the difference in signal as the copper blanket is removed and the barrier layer exposed. Then, the CNN models are trained with torque signal and CNN EPD system is established by Labview. For CMP of 40×40 mm2 copper blanket wafers and 8-inch copper blanket wafers, online CNN EPD system and offline CNN signal prediction are implemented. Results of CMP of 40×40 mm2 copper blanket wafers show that the CNN EPD are more accurate than the endpoint of material removal rate (MRR). Results of offline signal prediction of polishing head and plate show a well-fitting with mean square error of an average of 4.97×10^-7 and 9.61×10^-8 from CNN. Finally, the results of 8-inch copper blanket wafer CMP signal prediction show mean square error of an average of 8.97×10^-6 and 2.24×10^-7 respectively. The CNN can be verified effectively to predict the CMP process signal.

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